1 |
Liu, Sifei, et al. "Learning affinity via spatial propagation networks."Advances in Neural Information Processing Systems30 (2017). doi: https://doi.org/10.48550/arXiv.1710.01020
DOI
|
2 |
Ma, Fangchang, Guilherme Venturelli Cavalheiro, and Sertac Karaman. "Self-supervised sparse-to-dense: Self-supervised depth completion from lidar and monocular camera."2019 International Conference on Robotics and Automation (ICRA). IEEE, 2019. doi: https://doi.org/10.1109/ICRA.2019.8793637
DOI
|
3 |
He, Kaiming, et al. "Deep residual learning for image recognition. " Proceedings of the IEEE conference on computer vision and pattern recognition. 2016. doi: https://doi.org/10.1109/CVPR.2016.90
DOI
|
4 |
Ronneberger, Olaf, Philipp Fischer, and Thomas Brox. "U-net: Convolutional networks for biomedical image segmentation." International Conference on Medical image computing and computerassisted intervention. Springer, Cham, 2015. doi: https://doi.org/10.48550/arXiv.1505.04597
DOI
|
5 |
Qiu, Jiaxiong, et al. "Deeplidar: Deep surface normal guided depth prediction for outdoor scene from sparse lidar data and single color image."Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019. doi: https://doi.org/10.48550/arXiv.1812.00488
DOI
|
6 |
Eldesokey, Abdelrahman, Michael Felsberg, and Fahad Shahbaz Khan. "Confidence propagation through cnns for guided sparse depth regression."IEEE transactions on pattern analysis and machine intelligence 42.10 (2019): 2423-2436. doi: https://doi.org/10.1109/TPAMI.2019.2929170
DOI
|
7 |
Huang, Zixuan, et al. "Hms-net: Hierarchical multi-scale sparsityinvariant network for sparse depth completion."IEEE Transactions on Image Processing 29 (2019): 3429-3441. doi: https://doi.org/10.48550/arXiv.1808.08685
DOI
|
8 |
Zhang, Chongzhen, et al. "Multitask gans for semantic segmentation and depth completion with cycle consistency."IEEE Transactions on Neural Networks and Learning Systems32.12 (2021): 5404-5415. doi: https://doi.org/10.1109/TNNLS.2021.3072883
DOI
|
9 |
Nazir, Danish, et al. "SemAttNet: Towards Attention-based Semantic Aware Guided Depth Completion."arXiv preprint arXiv:2204.13635 (2022). doi: https://doi.org/10.1109/ACCESS.2022.3214316
DOI
|
10 |
Khan, Muhammad Ahmed Ullah, et al. "A Comprehensive Survey of Depth Completion Approaches." (2022). doi: https://doi.org/10.20944/preprints202205.0343.v1
DOI
|
11 |
Tang, Jie, et al. "Learning guided convolutional network for depth completion."IEEE Transactions on Image Processing 30 (2020): 1116-1129. doi: https://doi.org/10.1109/TIP.2020.3040528
DOI
|
12 |
Hu, Mu, et al. "Penet: Towards precise and efficient image guided depth completion."2021 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2021. doi: https://doi.org/10.48550/arXiv.2103.00783
DOI
|
13 |
Yan, Zhiqiang, et al. "RigNet: Repetitive image guided network for depth completion."arXiv preprint arXiv:2107.13802(2021).
|
14 |
Cheng, Xinjing, Peng Wang, and Ruigang Yang. "Learning depth with convolutional spatial propagation network."IEEE transactions on pattern analysis and machine intelligence 42.10 (2019): 2361-2379. doi: https://doi.org/10.48550/arXiv.1810.02695
DOI
|
15 |
Cheng, Xinjing, et al. "Cspn++: Learning context and resource aware convolutional spatial propagation networks for depth completion." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 34. No. 07. 2020. doi: https://doi.org/10.48550/arXiv.1911.05377
DOI
|
16 |
Xu, Zheyuan, Hongche Yin, and Jian Yao. "Deformable spatial propagation networks for depth completion."2020 IEEE International Conference on Image Processing (ICIP). IEEE, 2020. doi: https://doi.org/10.48550/arXiv.2007.04251
DOI
|
17 |
Kim, Jiwon, Jung Kwon Lee, and Kyoung Mu Lee. "Accurate image super-resolution using very deep convolutional networks."Proceedings of the IEEE conference on computer vision and pattern recognition. 2016. doi: https://doi.org/10.48550/arXiv.1511.04587
DOI
|
18 |
Park, Jinsun, et al. "Non-local spatial propagation network for depth completion."European Conference on Computer Vision. Springer, Cham, 2020. doi: https://doi.org/10.1007/978-3-030-58601-0_8
DOI
|
19 |
Lin, Yuankai, et al. "Dynamic spatial propagation network for depth completion."arXiv preprint arXiv:2202.09769(2022). doi: https://doi.org/10.48550/arXiv.2202.09769
DOI
|
20 |
Zhang, Kai, et al. "Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising."IEEE transactions on image processing 26.7 (2017): 3142-3155. doi: https://doi.org/10.1109/TIP.2017.2662206
DOI
|
21 |
Liu, Lina, et al. "Fcfr-net: Feature fusion based coarse-to-fine residual learning for depth completion."Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 35. No. 3. 2021. doi: https://doi.org/10.1609/aaai.v35i3.16311
DOI
|
22 |
Gu, Jiaqi, et al. "Denselidar: A real-time pseudo dense depth guided depth completion network."IEEE Robotics and Automation Letters6.2 (2021): 1808-1815. doi: https://doi.org/10.1109/LRA.2021.3060396
DOI
|
23 |
Zhu, Yufan, et al. "Robust depth completion with uncertainty-driven loss functions."Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 36. No. 3. 2022. doi: https://doi.org/10.1609/aaai.v36i3.20275
DOI
|
24 |
Lee, Byeong-Uk, Kyunghyun Lee, and In So Kweon. "Depth completion using plane-residual representation."Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021. doi: https://doi.org/10.48550/arXiv.2104.07350
DOI
|
25 |
Chodosh, Nathaniel, Chaoyang Wang, and Simon Lucey. "Deep convolutional compressed sensing for lidar depth completion."Asian Conference on Computer Vision. Springer, Cham, 2018. doi: https://doi.org/10.1007/978-3-030-20887-5_31
DOI
|
26 |
Silberman, Nathan, et al. "Indoor segmentation and support inference from rgbd images."European conference on computer vision. Springer, Berlin, Heidelberg, 2012. doi: https://doi.org/10.1007/978-3-642-33715-4_54
DOI
|
27 |
Uhrig, Jonas, et al. "Sparsity invariant cnns." 2017 international conference on 3D Vision (3DV). IEEE, 2017. doi: https://doi.org/10.1109/3DV.2017.00012
DOI
|